Prevalences of sleep-related breathing disorders and severity factors in chronic spinal cord injury and abled-bodied individuals undergoing rehabilitation: a comparative study.

STUDY OBJECTIVES: The objective of this study was to discern distinguishing characteristics of sleep-related breathing disorders in individuals with chronic spinal cord injury (CSCI) compared with participants without CSCI. Additionally, the study investigated factors associated with sleep-related breathing disorder severity. METHODS: This is a cross-sectional analysis of 123 individuals without CSCI, 40 tetraplegics, and 48 paraplegics who underwent attended or partially supervised full polysomnography for suspected sleep-related breathing disorders in a rehabilitation center. Polysomnographic, transcutaneous capnography, and clinical data were collected and compared between the groups. RESULTS: Among tetraplegics, apnea-hypopnea index ≥ 30 events/h (67.5%, P = .003), central apnea (17.5%, P = .007), and higher oxygen desaturation index (80.0%, P = .01) prevailed. Sleep-related hypoventilation was present in 15.4% of tetraplegics and 15.8% of paraplegics, compared with 3.2% in participants without CSCI (P = .05). In the group without CSCI and the paraplegic group, snoring and neck circumference were positively correlated with obstructive sleep apnea (OSA) severity. A positive correlation between waist circumference and OSA severity was identified in all groups, and multivariate logistic regression analysis showed that loud snoring and waist circumference had the greatest impact on OSA severity. CONCLUSIONS: Severe OSA and central sleep apnea prevailed in tetraplegic participants. Sleep-related hypoventilation was more common in tetraplegics and paraplegics than in participants without CSCI. Loud snoring and waist circumference had an impact on OSA severity in all groups. We recommend the routine implementation of transcutaneous capnography in individuals with CSCI. We underscore the significance of conducting a comprehensive sleep assessment in the rehabilitation process for individuals with CSCI. CITATION: Souza Bastos P, Amaral TLD, Yehia HC, Tavares A. Prevalences of sleep-related breathing disorders and severity factors in chronic spinal cord injury and abled-bodied individuals undergoing rehabilitation: a comparative study. J Clin Sleep Med. 2024;20(7):1119-1129.

Speech Analysis for Fatigue and Sleepiness Detection of a Pilot.

BACKGROUND: Mental fatigue and sleepiness are well recognized determinants of human-error related accidents and incidents in aviation. In Brazil, according to the Center for Investigation and Prevention of Aeronautical Accidents (CENIPA), the rate of accidents in the aerial modal is 1 per 2 d. Human factors are present in 90% of these accidents.CASE REPORT: This paper describes a retrospective study of the communication between a pilot and an air traffic control tower just before a fatal accident. The objective was the detection of fatigue and sleepiness of a pilot, who complained of these signs and symptoms before the flight, by means of voice and speech analysis. The in-depth accident analysis performed by CENIPA indicated that sleepiness and fatigue most likely contributed to the accident. Speech samples were analyzed for two conditions: 1) nonsleepy data recorded 35 h before the air crash (control condition), which were compared with 2) data from samples collected about 1 h before the accident and also during the disaster (sleepy condition). Audio recording analyses provided objective measures of the temporal organization of speech, such as hesitations, silent pauses, prolongation of final syllables, and syllable articulation rate.DISCUSSION: The results showed that speech during the day of the accident had significantly low elocution and articulation rates compared to the preceding day, also indicating that the methodology adopted in this study is feasible for detection of fatigue and sleepiness through speech analysis.de Vasconcelos CA, Vieira MN, Kecklund G, Yehia HC. Speech analysis for fatigue and sleepiness detection of a pilot. Aerosp Med Hum Perform. 2019; 90(4):415-418.

Brain activity underlying auditory perceptual learning during short period training: simultaneous fMRI and EEG recording.

BACKGROUND: There is an accumulating body of evidence indicating that neuronal functional specificity to basic sensory stimulation is mutable and subject to experience. Although fMRI experiments have investigated changes in brain activity after relative to before perceptual learning, brain activity during perceptual learning has not been explored. This work investigated brain activity related to auditory frequency discrimination learning using a variational Bayesian approach for source localization, during simultaneous EEG and fMRI recording. We investigated whether the practice effects are determined solely by activity in stimulus-driven mechanisms or whether high-level attentional mechanisms, which are linked to the perceptual task, control the learning process. RESULTS: The results of fMRI analyses revealed significant attention and learning related activity in left and right superior temporal gyrus STG as well as the left inferior frontal gyrus IFG. Current source localization of simultaneously recorded EEG data was estimated using a variational Bayesian method. Analysis of current localized to the left inferior frontal gyrus and the right superior temporal gyrus revealed gamma band activity correlated with behavioral performance. CONCLUSIONS: Rapid improvement in task performance is accompanied by plastic changes in the sensory cortex as well as superior areas gated by selective attention. Together the fMRI and EEG results suggest that gamma band activity in the right STG and left IFG plays an important role during perceptual learning.

Quantifying time-varying coordination of multimodal speech signals using correlation map analysis.

This paper demonstrates an algorithm for computing the instantaneous correlation coefficient between two signals. The algorithm is the computational engine for analyzing the time-varying coordination between signals, which is called correlation map analysis (CMA). Correlation is computed around any pair of points in the two input signals. Thus, coordination can be assessed across a continuous range of temporal offsets and be detected even when changing over time due to temporal fluctuations. The correlation algorithm has two major features: (i) it is structurally similar to a tunable filter, requiring only one parameter to set its cutoff frequency (and sensitivity), (ii) it can be applied either uni-directionally (computing correlation based only on previous samples) or bi-directionally (computing correlation based on both previous and future samples). Computing instantaneous correlation for a range of time offsets between two signals produces a 2D correlation map, in which correlation is characterized as a function of time and temporal offset. Graphic visualization of the correlation map provides rapid assessment of how correspondence patterns progress through time. The utility of the algorithm and of CMA are exemplified using the spatial and temporal coordination of various audible and visible components associated with linguistic performance.

Multivariate objective response detectors (MORD): statistical tools for multichannel EEG analysis during rhythmic stimulation.

The presence of cerebral evoked responses can be tested by using objective response detectors. They are statistical tests that provide a threshold above which responses can be assumed to have occurred. The detection power depends on the signal-to-noise ratio (SNR) of the response and the amount of data available. However, the correlation within the background noise could also affect the power of such detectors. For a fixed SNR, the detection can only be improved at the expense of using a longer stretch of signal. This can constitute a limitation, for instance, in monitored surgeries. Alternatively, multivariate objective response detection (MORD) could be used. This work applies two MORD techniques (multiple coherence and multiple component synchrony measure) to EEG data collected during intermittent photic stimulation. They were evaluated throughout Monte Carlo simulations, which also allowed verifying that correlation in the background reduces the detection rate. Considering the N EEG derivations as close as possible to the primary visual cortex, if N = 4, 6 or 8, multiple coherence leads to a statistically significant higher detection rate in comparison with multiple component synchrony measure. With the former, the best performance was obtained with six signals (O1, O2, T5, T6, P3 and P4).

Avoiding spectral leakage in objective detection of auditory steady-state evoked responses in the inferior colliculus of rat using coherence.

Local field potentials (LFP) are bioelectric signals recorded from the brain that reflect neural activity in a high temporal resolution. Separating background activity from that evoked by specific somato-sensory input is a matter of great clinical relevance in neurology. The coherence function is a spectral coefficient that can be used as a detector of periodic responses in noisy environments. Auditory steady-state responses to amplitude-modulated tones generate periodic responses in neural networks that may be accessed by means of coherence between the stimulation signal and the LFP recorded from the auditory pathway. Such signal processing methodology was applied in this work to evaluate in vivo, anaesthetized Wistar rats, activation of neural networks due to single carrier sound stimulation frequencies, as well as to evaluate the effect of different modulating tones in the evoked responses. Our results show that an inappropriate choice of sound stimuli modulating frequencies can compromise coherence analysis, e.g. misleading conclusions due to mathematical artefact of signal processing. Two modulating frequency correction protocols were used: nearest integer and nearest prime number. The nearest prime number correction was successful in avoiding spectral leakage in the coherence analysis of steady-state auditory response, as predicted by Monte Carlo simulations.